scRNA-seq analysis using Seurat package
Long Nguyen
Description:
- The purpose of this notebook is to analyze scRNA-seq data of mouse Estrus
- The method used is reading directly from fastq files
- The algorithms used come from Seurat package
- The results will compared to:
- Same dataset analyzed using Scanpy - read10x
- Same dataset analyzed using Scanpy - readloom
Workflow begin
This tutorial comes from Seurat
Load required packages
library(dplyr)
library(Seurat)
library(leiden)
library(ggplot2)Load data
Input is a directory that contains fastq files
previous_time <- proc.time()[3]
Estrus.data <-Read10X(data.dir ="/home/nguyen/Estrus_cellranger_count/Estrus_Raw/")
proc.time()[3] - previous_timeelapsed
8.09 - View(Estrus.data) - run this command to investigate the output
- Current dataset:
- 31253 genes
- 6599 cells
Initialize the Seurat object
- Initialize the Seurat object with the raw (non-normalized data).
- Filter out genes that are detected in less than 3 cells
- Filter out cells that have less than 200 genes expressed
previous_time <- proc.time()[3]
Estrus <- CreateSeuratObject(counts = Estrus.data, project = "Estrus", min.cells = 3, min.features = 200)
proc.time()[3] - previous_timeelapsed
1.876 EstrusAn object of class Seurat
19420 features across 6481 samples within 1 assay
Active assay: RNA (19420 features, 0 variable features)*Output: + 19420 genes + 6481 cells
Calculate the percentage of mitochondrial genes
- Add “percent.mt” column to object metadata
- Percentage of mitochondrial genes is stored in “percent.mt”
- “^mt-” means strings start with mt (lowercase mt for mice, uppercase MT for human)
Estrus[["percent.mt"]] <- PercentageFeatureSet(Estrus, pattern = "^mt-")# This is the list of genes that start with mt
mito_genes <- grep("^mt-", rownames(Estrus) , ignore.case=T, value=T)
print(mito_genes) [1] "mt-Nd1" "mt-Nd2" "mt-Co1" "mt-Co2" "mt-Atp8" "mt-Atp6" "mt-Co3" "mt-Nd3" "mt-Nd4l" "mt-Nd4" "mt-Nd5" "mt-Nd6" "mt-Cytb"# This is the data in "percent.mt" column
percent_mito <- FetchData(object = Estrus, vars = "percent.mt") #how to fetch data
head(percent_mito)# Visualize QC metrics as a violin plot
VlnPlot(Estrus, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(Estrus, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(Estrus, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Filter out low-quality cells
- We filter out cells that have unique feature counts over 6000
- Because any cell that has more than 6000 genes are likely doublets
- Note: Seurat tutorial used 2500 instead of 6000
- We also filter out cells that have more than 5% mitochondrial genes
- Because those are likely to be nonviable cells
previous_time <- proc.time()[3]
Estrus <- subset(Estrus, subset = nFeature_RNA < 6000 & percent.mt < 5)
proc.time()[3] - previous_timeelapsed
0.235 EstrusAn object of class Seurat
19420 features across 2174 samples within 1 assay
Active assay: RNA (19420 features, 0 variable features)- After filtering step, the dataset is left with
- 19420 genes
- 2174 cells
Normalizing the data
- Perform log-normalization method
- Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor
- scale.factor default value = 10,000
- This is then natural-log transformed using log1p
previous_time <- proc.time()[3]
Estrus <- NormalizeData(Estrus, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|proc.time()[3] - previous_timeelapsed
0.223 Identification of highly variable features (feature selection)
previous_time <- proc.time()[3]
Estrus <- FindVariableFeatures(Estrus, selection.method = "disp", nfeatures = 5000)Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|proc.time()[3] - previous_timeelapsed
0.4 Plotting highly variable features
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(Estrus), 10)
top10 [1] "Hbb-bs" "Hba-a1" "Hba-a2" "Dcpp3" "Hbb-bt" "Ccl21a" "Apoe" "Cd74" "Ccl5" "Dcpp1" #{r, fig.width=10,fig.height=11}
#df %>% ggplot(aes(x = x, y = y)) + geom_point()
# Plot variable features with and without labels
plot1 <- VariableFeaturePlot(Estrus)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2 Warning: Removed 2 rows containing missing values (geom_point).
Warning: Removed 2 rows containing missing values (geom_point).
Regress out data and perform scaling
- Regressing out total counts per cell and percent.mt before scaling.
- Take out mitochondrial genes before scaling (analyzing) the data
- Because mito_genes shouldn’t be accounted for cell population analysis.
previous_time <- proc.time()[3]
all.genes <- rownames(Estrus)
Estrus <- ScaleData(Estrus, features = all.genes, vars.to.regress = c("nCount_RNA","percent.mt"), scale.max = 10)Regressing out nCount_RNA, percent.mt
|
| | 0%
|
|= | 0%
|
|= | 1%
|
|== | 1%
|
|== | 2%
|
|=== | 2%
|
|=== | 3%
|
|==== | 3%
|
|==== | 4%
|
|===== | 4%
|
|====== | 4%
|
|====== | 5%
|
|======= | 5%
|
|======= | 6%
|
|======== | 6%
|
|======== | 7%
|
|========= | 7%
|
|========= | 8%
|
|========== | 8%
|
|=========== | 8%
|
|=========== | 9%
|
|============ | 9%
|
|============ | 10%
|
|============= | 10%
|
|============= | 11%
|
|============== | 11%
|
|============== | 12%
|
|=============== | 12%
|
|================ | 13%
|
|================= | 13%
|
|================= | 14%
|
|================== | 14%
|
|================== | 15%
|
|=================== | 15%
|
|=================== | 16%
|
|==================== | 16%
|
|==================== | 17%
|
|===================== | 17%
|
|====================== | 17%
|
|====================== | 18%
|
|======================= | 18%
|
|======================= | 19%
|
|======================== | 19%
|
|======================== | 20%
|
|========================= | 20%
|
|========================= | 21%
|
|========================== | 21%
|
|=========================== | 21%
|
|=========================== | 22%
|
|============================ | 22%
|
|============================ | 23%
|
|============================= | 23%
|
|============================= | 24%
|
|============================== | 24%
|
|============================== | 25%
|
|=============================== | 25%
|
|================================ | 25%
|
|================================ | 26%
|
|================================= | 26%
|
|================================= | 27%
|
|================================== | 27%
|
|================================== | 28%
|
|=================================== | 28%
|
|=================================== | 29%
|
|==================================== | 29%
|
|===================================== | 29%
|
|===================================== | 30%
|
|====================================== | 30%
|
|====================================== | 31%
|
|======================================= | 31%
|
|======================================= | 32%
|
|======================================== | 32%
|
|======================================== | 33%
|
|========================================= | 33%
|
|========================================== | 33%
|
|========================================== | 34%
|
|=========================================== | 34%
|
|=========================================== | 35%
|
|============================================ | 35%
|
|============================================ | 36%
|
|============================================= | 36%
|
|============================================= | 37%
|
|============================================== | 37%
|
|=============================================== | 38%
|
|================================================ | 38%
|
|================================================ | 39%
|
|================================================= | 39%
|
|================================================= | 40%
|
|================================================== | 40%
|
|================================================== | 41%
|
|=================================================== | 41%
|
|=================================================== | 42%
|
|==================================================== | 42%
|
|===================================================== | 42%
|
|===================================================== | 43%
|
|====================================================== | 43%
|
|====================================================== | 44%
|
|======================================================= | 44%
|
|======================================================= | 45%
|
|======================================================== | 45%
|
|======================================================== | 46%
|
|========================================================= | 46%
|
|========================================================== | 46%
|
|========================================================== | 47%
|
|=========================================================== | 47%
|
|=========================================================== | 48%
|
|============================================================ | 48%
|
|============================================================ | 49%
|
|============================================================= | 49%
|
|============================================================= | 50%
|
|============================================================== | 50%
|
|=============================================================== | 50%
|
|=============================================================== | 51%
|
|================================================================ | 51%
|
|================================================================ | 52%
|
|================================================================= | 52%
|
|================================================================= | 53%
|
|================================================================== | 53%
|
|================================================================== | 54%
|
|=================================================================== | 54%
|
|==================================================================== | 54%
|
|==================================================================== | 55%
|
|===================================================================== | 55%
|
|===================================================================== | 56%
|
|====================================================================== | 56%
|
|====================================================================== | 57%
|
|======================================================================= | 57%
|
|======================================================================= | 58%
|
|======================================================================== | 58%
|
|========================================================================= | 58%
|
|========================================================================= | 59%
|
|========================================================================== | 59%
|
|========================================================================== | 60%
|
|=========================================================================== | 60%
|
|=========================================================================== | 61%
|
|============================================================================ | 61%
|
|============================================================================ | 62%
|
|============================================================================= | 62%
|
|============================================================================== | 63%
|
|=============================================================================== | 63%
|
|=============================================================================== | 64%
|
|================================================================================ | 64%
|
|================================================================================ | 65%
|
|================================================================================= | 65%
|
|================================================================================= | 66%
|
|================================================================================== | 66%
|
|================================================================================== | 67%
|
|=================================================================================== | 67%
|
|==================================================================================== | 67%
|
|==================================================================================== | 68%
|
|===================================================================================== | 68%
|
|===================================================================================== | 69%
|
|====================================================================================== | 69%
|
|====================================================================================== | 70%
|
|======================================================================================= | 70%
|
|======================================================================================= | 71%
|
|======================================================================================== | 71%
|
|========================================================================================= | 71%
|
|========================================================================================= | 72%
|
|========================================================================================== | 72%
|
|========================================================================================== | 73%
|
|=========================================================================================== | 73%
|
|=========================================================================================== | 74%
|
|============================================================================================ | 74%
|
|============================================================================================ | 75%
|
|============================================================================================= | 75%
|
|============================================================================================== | 75%
|
|============================================================================================== | 76%
|
|=============================================================================================== | 76%
|
|=============================================================================================== | 77%
|
|================================================================================================ | 77%
|
|================================================================================================ | 78%
|
|================================================================================================= | 78%
|
|================================================================================================= | 79%
|
|================================================================================================== | 79%
|
|=================================================================================================== | 79%
|
|=================================================================================================== | 80%
|
|==================================================================================================== | 80%
|
|==================================================================================================== | 81%
|
|===================================================================================================== | 81%
|
|===================================================================================================== | 82%
|
|====================================================================================================== | 82%
|
|====================================================================================================== | 83%
|
|======================================================================================================= | 83%
|
|======================================================================================================== | 83%
|
|======================================================================================================== | 84%
|
|========================================================================================================= | 84%
|
|========================================================================================================= | 85%
|
|========================================================================================================== | 85%
|
|========================================================================================================== | 86%
|
|=========================================================================================================== | 86%
|
|=========================================================================================================== | 87%
|
|============================================================================================================ | 87%
|
|============================================================================================================= | 88%
|
|============================================================================================================== | 88%
|
|============================================================================================================== | 89%
|
|=============================================================================================================== | 89%
|
|=============================================================================================================== | 90%
|
|================================================================================================================ | 90%
|
|================================================================================================================ | 91%
|
|================================================================================================================= | 91%
|
|================================================================================================================= | 92%
|
|================================================================================================================== | 92%
|
|=================================================================================================================== | 92%
|
|=================================================================================================================== | 93%
|
|==================================================================================================================== | 93%
|
|==================================================================================================================== | 94%
|
|===================================================================================================================== | 94%
|
|===================================================================================================================== | 95%
|
|====================================================================================================================== | 95%
|
|====================================================================================================================== | 96%
|
|======================================================================================================================= | 96%
|
|======================================================================================================================== | 96%
|
|======================================================================================================================== | 97%
|
|========================================================================================================================= | 97%
|
|========================================================================================================================= | 98%
|
|========================================================================================================================== | 98%
|
|========================================================================================================================== | 99%
|
|=========================================================================================================================== | 99%
|
|=========================================================================================================================== | 100%
|
|============================================================================================================================| 100%
Centering and scaling data matrix
|
| | 0%
|
|====== | 5%
|
|============ | 10%
|
|=================== | 15%
|
|========================= | 20%
|
|=============================== | 25%
|
|===================================== | 30%
|
|=========================================== | 35%
|
|================================================== | 40%
|
|======================================================== | 45%
|
|============================================================== | 50%
|
|==================================================================== | 55%
|
|========================================================================== | 60%
|
|================================================================================= | 65%
|
|======================================================================================= | 70%
|
|============================================================================================= | 75%
|
|=================================================================================================== | 80%
|
|========================================================================================================= | 85%
|
|================================================================================================================ | 90%
|
|====================================================================================================================== | 95%
|
|============================================================================================================================| 100%# Explanations below are from [Seurat](https://satijalab.org/seurat/archive/v3.0/cell_cycle_vignette.html)
# vars.to.regress = RegressOut in Seurat version 1.4 or lower
# However, as the results of this procedure are stored in the scaled data slot (therefore overwriting the output of ScaleData)
# This functionality is merged into the ScaleData function itself.
proc.time()[3] - previous_timeelapsed
549.548 Perform linear dimensional reduction (PCA)
previous_time <- proc.time()[3]
Estrus <- RunPCA(Estrus, features = VariableFeatures(object = Estrus))PC_ 1
Positive: Gm867, 1700016K19Rik, 1700007K13Rik, Ccdc153, Tmem212, Gm19935, Dynlrb2, 1110017D15Rik, Cfap126, Fam166c
Fam183b, Vpreb3, Rsph1, Dnali1, Foxj1, Lrrc10b, Sntn, Tctex1d4, 1700024G13Rik, Mlf1
Pifo, Dnah12, Prr29, Hdc, 2410004P03Rik, Timp4, Efcab10, Tppp3, 2610028H24Rik, Lrrc51
Negative: Rps28, Rps19, Rpl18a, Rpl23a, Rplp0, Rpl13, Rpl37, Rpl32, Rps12, Rps18
Rps5, Rps29, Rps24, Rpl37a, Rps7, Rps27a, Rps23, Cd63, Rpl35, Rpl39
Rps4x, Rps11, Rpl27a, Rps20, Rps15a, Rps16, Rps3a1, Rpl19, Rpl10, Rps2
PC_ 2
Positive: Cd68, Lcn2, Sprr2f, Arg1, Wfdc2, Sftpd, Pdzk1ip1, Prap1, Cxcl17, Clca1
Serpina1e, Fxyd3, Pigr, Ltf, Tmem45b, Ctss, Padi2, Tacstd2, Ly6a, Muc4
Padi1, Ckmt1, Trpv6, Fxyd4, Muc1, Pglyrp1, Rnf128, Cndp2, Car2, Alox5ap
Negative: Serpinh1, Sparc, Col1a2, Col3a1, Col1a1, Col6a1, Cald1, Bgn, Mgp, Aebp1
Col6a2, Igfbp7, Rarres2, Zbtb20, Dcn, Nbl1, Rcn3, Igfbp6, Mdk, Sparcl1
Fxyd1, Mfap2, Csrp2, Pbx1, Fxyd6, Col6a3, Col4a1, Gsn, Lgals1, Htra3
PC_ 3
Positive: Tyrobp, Fcer1g, Ctss, Cd52, Csf1r, Cd74, H2-Aa, H2-Ab1, Laptm5, H2-Eb1
Aif1, Coro1a, Apoe, C1qc, Lyz2, C1qa, C1qb, H2-DMa, Pld4, Ms4a6c
Cx3cr1, Cybb, Ly86, Spi1, Ms4a7, Mpeg1, Trf, Fcgr3, Lst1, Fyb
Negative: Plet1, Ovgp1, Krt18, Wfdc18, Spint2, Aldoc, Gstm2, Epcam, Emb, Kctd14
Tpd52l1, Clu, Fxyd3, Dcxr, Kcnk1, Hddc2, Agr2, Rbp1, Psmd3, Cd9
Cldn22, Cystm1, Cldn7, Ppp1r1b, Gstm1, Rpl41, Mgst1, Tmem218, Krt19, Krt8
PC_ 4
Positive: Clca1, Cxcl17, Pigr, Plac8, Muc4, Prap1, Slc40a1, Ly6a, Serpina1e, Tmem45b
Sprr2f, Trpv6, Tacstd2, Tmprss4, Tspan1, Prxl2a, Arg1, Padi2, Fxyd4, Fcgbp
Fut9, Lrg1, Ckmt1, Morrbid, Car2, Fbln1, Upk3bl, Ncoa7, Pdzk1ip1, Sult1d1
Negative: Kctd14, Gstm2, Ovgp1, Emb, Sdf2l1, Clu, Ctsc, Cbs, Tmem218, Gstm1
Slc1a3, Ppp1r1b, Smim14, Lrpap1, Shisa8, Cldn22, Fn1, Agr2, Wfdc18, Rcn1
Dkk3, Ncf2, Bex4, Gatm, Ccnd1, Gdpd2, Aldoc, Manf, Selenom, Cyth4
PC_ 5
Positive: Gsn, Pdgfra, Htra3, Mfap4, Dpt, Mfap2, Serpinf1, Ntrk2, Col6a3, Mdk
Lpl, Penk, Lama2, Matn2, Col15a1, Gpm6b, Lrp1, Col1a1, Col5a2, Sparcl1
Ramp3, Htra1, Ftl1, Spon1, Ramp1, Islr, Col1a2, Plxdc2, Rxfp1, Osr2
Negative: Egfl7, Kdr, Ecscr, Cavin2, Pecam1, Cldn5, Cav1, Cdh5, Tie1, Mmrn1
Emcn, Flt4, Ushbp1, Mmrn2, Ccl21a, Podxl, Gimap6, S1pr1, Reln, Prox1
Adgrf5, Myct1, Gpm6a, Esam, Pard6g, Lyve1, Gimap1, Stmn2, Gimap4, Arhgap29 proc.time()[3] - previous_timeelapsed
5.29 Examine and visualize PCA results a few different ways
print(Estrus[["pca"]], dims = 1:5, nfeatures = 5)PC_ 1
Positive: Gm867, 1700016K19Rik, 1700007K13Rik, Ccdc153, Tmem212
Negative: Rps28, Rps19, Rpl18a, Rpl23a, Rplp0
PC_ 2
Positive: Cd68, Lcn2, Sprr2f, Arg1, Wfdc2
Negative: Serpinh1, Sparc, Col1a2, Col3a1, Col1a1
PC_ 3
Positive: Tyrobp, Fcer1g, Ctss, Cd52, Csf1r
Negative: Plet1, Ovgp1, Krt18, Wfdc18, Spint2
PC_ 4
Positive: Clca1, Cxcl17, Pigr, Plac8, Muc4
Negative: Kctd14, Gstm2, Ovgp1, Emb, Sdf2l1
PC_ 5
Positive: Gsn, Pdgfra, Htra3, Mfap4, Dpt
Negative: Egfl7, Kdr, Ecscr, Cavin2, Pecam1 VizDimLoadings(Estrus, dims = 1:2, reduction = "pca")
DimPlot(Estrus, reduction = "pca") + ggtitle(label = 'Including immune cells')
FeaturePlot(Estrus, reduction = "pca", features = c('Ovgp1',
'Foxj1', 'Igf1', 'Pdxk',
'Fxyd4', 'Bsg',
'Serpina1e', 'Krt19'))
FeaturePlot(Estrus, reduction = "pca", features = c('Dcn', 'Pax8',
'Ephx2',
'Pecam1', 'Pdgfra', 'Myh11', 'Atp2b4',
'Cd52'))
Determine the ‘dimensionality’ of the data
- Seurat tutorial uses JackStraw() and and ScoreJackStraw to determine dimensionality
- the syntax is:
- Estrus <- JackStraw(Estrus, num.replicate = 100)
- Estrus <- ScoreJackStraw(Estrus, dims = 1:20)
- However, it takes very long.
- An elbow plot often corresponds well enough with the significant dims and is much faster to run than Jackstraw
- According to the graph below, choose the first 37 principle components to use
ElbowPlot(Estrus, ndims = 40)
Find neighbors
- Dims = dimensions = determined as 37 from the above step
previous_time <- proc.time()[3]
Estrus_new <- FindNeighbors(Estrus, dims = 1:37, k.param = 10)Computing nearest neighbor graphComputing SNNproc.time()[3] - previous_timeelapsed
0.307 Find cell clusters using Leiden’s method
# Algorithm = 4 specifies Leiden's method
previous_time <- proc.time()[3]
Estrus_new <- FindClusters(Estrus_new, resolution = 0.12, algorithm = 4)
proc.time()[3] - previous_timeelapsed
0.559 Constructing UMAP plot
previous_time <- proc.time()[3]
Estrus_new <- RunUMAP(Estrus_new, dim = 1:37) 18:28:21 UMAP embedding parameters a = 0.9922 b = 1.112
18:28:21 Read 2174 rows and found 37 numeric columns
18:28:21 Using Annoy for neighbor search, n_neighbors = 30
18:28:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:28:21 Writing NN index file to temp file /tmp/RtmpdPzDpp/filec7e289291cc
18:28:21 Searching Annoy index using 1 thread, search_k = 3000
18:28:22 Annoy recall = 100%
18:28:22 Commencing smooth kNN distance calibration using 1 thread
18:28:23 Initializing from normalized Laplacian + noise
18:28:23 Commencing optimization for 500 epochs, with 85588 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:28:25 Optimization finishedproc.time()[3] - previous_timeelapsed
4.369 Visualization
DimPlot(Estrus_new, reduction = "umap") + ggtitle(label = 'Including immune cells')
FeaturePlot(Estrus_new, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Dcn', 'Serpina1e', 'Sprr2f',
'Fxyd4', 'Ly6a', 'Epcam', 'Krt19', 'Vim', 'Pdgfra'))
FeaturePlot(Estrus_new, reduction = "umap", features = c('Myh11',
'Dkk2', 'Slitrk6', 'Dio2', 'Smoc2', 'Tes', 'Dab1', 'Camk1d', 'Pecam1', 'Cd52','Mki67'))
Removing immune cells
sub_Estrus = subset(x = Estrus_new, ident = c(1,2,3,4,5,7,8,9,10,12))
DimPlot(sub_Estrus, reduction = "umap") + ggtitle(label = 'Immune cells removed')
FeaturePlot(sub_Estrus, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Fxyd4', 'Cxcl17', 'Myh11', 'Cd52', 'Twist2', 'Pecam1'))
FeaturePlot(sub_Estrus, reduction = "umap", features = c('Pdgfra', 'Ephx2', 'Dcn', 'Krt19', 'Epcam', 'Vim','Mki67'))
sub_EstrusAn object of class Seurat
19420 features across 1925 samples within 1 assay
Active assay: RNA (19420 features, 5000 variable features)
2 dimensional reductions calculated: pca, umap- After removing immune cells, the dataset is left with:
- 19420 genes
- 1925 cells
Re-run analysis after excluding immune cells
previous_time <- proc.time()[3]
sub_Estrus <- RunPCA(sub_Estrus, features = VariableFeatures(object = sub_Estrus))PC_ 1
Positive: Rps28, Rps19, Rplp0, Rpl13, Rpl18a, Rpl23a, Rpl37, Rpl32, Rps18, Rps5
Cd63, Rps12, Rps7, Rpl37a, Rps24, Rpl39, Rps27a, Rps23, Rps4x, Rps29
Rpl35, Rps20, Rpl27a, Rps3a1, Rps15a, Rps11, Rps2, Rpl10a, Rps16, Rpl10
Negative: Gm867, 1700016K19Rik, 1700007K13Rik, Ccdc153, Tmem212, Gm19935, Dynlrb2, Fam166c, 1110017D15Rik, Cfap126
Fam183b, Sntn, Foxj1, Dnali1, Vpreb3, Rsph1, Lrrc10b, Tctex1d4, Dnah12, 1700024G13Rik
Mlf1, Pifo, Prr29, 2410004P03Rik, Tppp3, Hdc, Dnaja4, Efcab10, Cfap77, Timp4
PC_ 2
Positive: Lcn2, Fxyd3, Ltf, Wfdc2, Sftpd, Arg1, Pdzk1ip1, Sprr2f, Prap1, Epcam
Cxcl17, Serpina1e, Clca1, Muc1, Padi1, Rnf128, Pigr, Plet1, Elf3, Tmem45b
Ly6a, Tacstd2, Muc4, Ckmt1, Cldn7, Car2, Fxyd4, Pglyrp1, Trpv6, Tspan8
Negative: Col1a2, Serpinh1, Aebp1, Col3a1, Cald1, Col6a1, Col1a1, Sparc, Mgp, Rarres2
Bgn, Col6a2, Dcn, Rcn3, Igfbp6, Rnase4, Igfbp7, Zbtb20, Lgals1, Mdk
Nbl1, Csrp2, Igf1, Fxyd1, Lrp1, Sparcl1, Mfap2, Col6a3, Gsn, Timp2
PC_ 3
Positive: Ovgp1, Kctd14, Gstm2, Emb, Clu, Wfdc18, Gstm1, Tmem218, Cbs, Aldoc
Ppp1r1b, Cldn22, Agr2, Slc1a3, Lrpap1, Shisa8, Tpd52l1, Rcn1, Dkk3, Sdf2l1
Fn1, Bex4, Manf, Gdpd2, Smim14, Selenom, Kcne3, Enho, Slc15a2, Slco2a1
Negative: Plac8, Clca1, Pigr, Muc4, Cxcl17, Slc40a1, Ly6a, Prap1, Tspan1, Tmem45b
Trpv6, Tmprss4, Serpina1e, Sprr2f, Tacstd2, Padi2, Prxl2a, Fcgbp, Fbln1, Cfh
Fut9, Fxyd4, Arg1, Rasa4, Lrg1, Morrbid, Ncoa7, Ckmt1, Slc39a8, Upk3bl
PC_ 4
Positive: Upk3b, Nkain4, Lrrn4, Upk1b, C2, Myrf, Lgals2, Fgf1, Lgals7, Smoc2
Gpm6a, Serpinb6b, Slpi, Cldn15, Ogn, Cybrd1, Plxna4, Rspo1, Nrgn, Msln
Aldh1a2, Rpp25, Ptgis, Hpgd, Adam33, Prph, Lmcd1, Tm4sf5, Hacd4, Prss23
Negative: Pdgfra, Sparcl1, Gsn, Dpt, Mfap4, Htra3, Mfap2, Col15a1, Itih5, Gpm6b
Ramp3, Lama2, Ntrk2, Penk, G0s2, Serpinf1, Lpl, Spon1, Col6a3, Enpp2
Rxfp1, Pdgfrl, Matn2, Lhfp, Ctsk, Plpp3, Ramp1, P2ry1, Col5a3, Nrp1
PC_ 5
Positive: Dcn, Aebp1, Ctsl, Pdgfra, Dpt, Pcolce, Fbln1, Lpl, Cygb, Ramp3
Col15a1, Ntrk2, Htra1, Meg3, Plxdc2, Htra3, Lum, Col5a3, Efemp1, Rnase4
Itm2a, Col3a1, Xdh, Cdh11, P2ry1, Ctsk, C1s1, Itm2b, Col6a2, Dkk2
Negative: Myh11, Cnn1, Lmod1, Fbxl22, Acta2, Mustn1, Actg2, Tagln, Sh3bgr, Mcam
Des, Mylk, Nexn, Myl9, Ppp1r14a, Slc22a3, Tpm2, Aoc3, Myom1, Synpo2
Postn, Carmn, Smtn, Dmpk, Ppp1r12b, Tpm1, Pgm5, Rbpms2, Slc2a4, Pdlim3 proc.time()[3] - previous_timeelapsed
4.379 DimPlot(sub_Estrus, reduction = "pca") + ggtitle(label = 'Immune cells removed')
FeaturePlot(sub_Estrus, reduction = "pca", features = c('Esr1', 'Ovgp1', 'Foxj1', 'Fxyd4', 'Ephx2', 'Dcn'))
FeaturePlot(sub_Estrus, reduction = "pca", features = c('Fos', 'Kcne3', 'Serpina1e', 'Crabp2', 'Pgr', 'Vim'))
previous_time <- proc.time()[3]
sub_Estrus_new <- FindNeighbors(sub_Estrus, dims = 1:37)Computing nearest neighbor graphComputing SNNproc.time()[3] - previous_timeelapsed
0.306 previous_time <- proc.time()[3]
sub_Estrus_new <- FindClusters(sub_Estrus_new, resolution = 0.045, algorithm = 4)
proc.time()[3] - previous_timeelapsed
0.592 previous_time <- proc.time()[3]
sub_Estrus_new <- RunUMAP(sub_Estrus_new, dim = 1:37)18:31:18 UMAP embedding parameters a = 0.9922 b = 1.112
18:31:18 Read 1925 rows and found 37 numeric columns
18:31:18 Using Annoy for neighbor search, n_neighbors = 30
18:31:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:31:18 Writing NN index file to temp file /tmp/RtmpdPzDpp/filec7e27d32c007
18:31:18 Searching Annoy index using 1 thread, search_k = 3000
18:31:19 Annoy recall = 100%
18:31:19 Commencing smooth kNN distance calibration using 1 thread
18:31:20 Initializing from normalized Laplacian + noise
18:31:20 Commencing optimization for 500 epochs, with 77854 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:31:22 Optimization finishedproc.time()[3] - previous_timeelapsed
4.1 DimPlot(sub_Estrus_new, reduction = "umap") + ggtitle(label = 'Rescale UMAP after removing Immune cells')
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Crabp2', 'Myh11', 'Wt1', 'S100g', 'Sprr2f',
'Fxyd4'))
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Dcn', 'Pax8', 'Pax2', 'Twist2', 'Igf1', 'Serpina1e', 'Pdgfra', 'Krt19', 'Mki67'))
Marker genes
marker_genes = c('Epcam', 'Krt15','Krt8','Krt19','Vim','Pdgfra','Twist2', 'Sprr2f', 'Fxyd4', 'Igfbp6',
'Acta2', 'Myh11','Foxj1','Ovgp1','Wt1','Serpina1e','S100g','Pecam1')UMAP for marker genes
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Epcam', 'Krt15','Krt8','Krt19','Vim','Pdgfra','Twist2', 'Sprr2f', 'Fxyd4'))
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Igfbp6',
'Acta2', 'Myh11','Foxj1','Ovgp1','Wt1','Serpina1e','S100g','Pecam1'))
Rename clusters
Cluster.names <- c('1 Secretory_InfAmp',
'2 Ciliated_InfAmp',
'3 Fibroblast Pdgfra+',
'4 Secretory_IsthUTJ',
'5 Fibroblast Pdgfra-',
'6 Muscle')
names(Cluster.names) <- levels(sub_Estrus_new)
sub_Estrus_new <- RenameIdents(sub_Estrus_new, Cluster.names)
DimPlot(sub_Estrus_new, reduction = "umap", pt.size = 0.5) + scale_color_manual(labels = Cluster.names,values=c('green', 'red','blue','orange','grey','yellow','pink'))
DotPlot(sub_Estrus_new, features = marker_genes, group.by = sub_Estrus_new@meta.data$ident) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_y_discrete(labels = Cluster.names)
Number of cells in each clusters
table(sub_Estrus_new@active.ident, sub_Estrus_new@meta.data$orig.ident)
Estrus
1 Secretory_InfAmp 603
2 Ciliated_InfAmp 502
3 Fibroblast Pdgfra+ 330
4 Secretory_IsthUTJ 271
5 Fibroblast Pdgfra- 129
6 Muscle 90Extract top genes for each cluster
- Default method of finding highly expressed genes for FindAllMarkers is Wilcoxon Rank Sum test
sub_Estrus_new_markers <- FindAllMarkers(sub_Estrus_new, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 1 Secretory_InfAmp
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 2 Ciliated_InfAmp
| | 0 % ~calculating
|+ | 1 % ~10s
|+ | 2 % ~09s
|++ | 3 % ~09s
|++ | 4 % ~09s
|+++ | 5 % ~09s
|+++ | 6 % ~09s
|++++ | 7 % ~09s
|++++ | 8 % ~09s
|+++++ | 9 % ~09s
|+++++ | 10% ~09s
|++++++ | 11% ~09s
|++++++ | 12% ~08s
|+++++++ | 13% ~08s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|+++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|+++++++++++ | 22% ~08s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~06s
|+++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s Calculating cluster 3 Fibroblast Pdgfra+
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 4 Secretory_IsthUTJ
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~04s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 5 Fibroblast Pdgfra-
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 6 Muscle
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s sub_Estrus_new_markers %>%
group_by(cluster) %>%
slice_max(n = 20, order_by = avg_log2FC)Heat map for top 10 genes in each cluster
sub_Estrus_new_markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10
DoHeatmap(sub_Estrus_new, features = top10$gene) + NoLegend()
---
title: "scRNA-seq analysis using Seurat package"
output: html_notebook
author: Long Nguyen
---
# Description:
* The purpose of this notebook is to analyze scRNA-seq data of mouse Estrus
* The method used is reading directly from fastq files
* The algorithms used come from Seurat package 
* The results will compared to:
  + Same dataset analyzed using Scanpy - read10x
  + Same dataset analyzed using Scanpy - readloom
  
# Workflow begin
This tutorial comes from [Seurat](https://satijalab.org/seurat/articles/pbmc3k_tutorial.html)

## Load required packages
```{r}
library(dplyr)
library(Seurat)
library(leiden)
library(ggplot2)
```


## Load data
Input is a directory that contains fastq files
```{r}
previous_time <- proc.time()[3]
Estrus.data <-Read10X(data.dir ="/home/nguyen/Estrus_cellranger_count/Estrus_Raw/")
proc.time()[3] - previous_time
```
* View(Estrus.data) - run this command to investigate the output
* Current dataset:
  + 31253 genes
  + 6599 cells

## Initialize the Seurat object
* Initialize the Seurat object with the raw (non-normalized data).
* Filter out genes that are detected in less than 3 cells 
* Filter out cells that have less than 200 genes expressed
```{r}
previous_time <- proc.time()[3]
Estrus <- CreateSeuratObject(counts = Estrus.data, project = "Estrus", min.cells = 3, min.features = 200)
proc.time()[3] - previous_time
Estrus
```
*Output:
  + 19420 genes 
  + 6481 cells

## Calculate the percentage of mitochondrial genes
* Add "percent.mt" column to object metadata
* Percentage of mitochondrial genes is stored in "percent.mt"
* "^mt-" means strings start with mt (lowercase mt for mice, uppercase MT for human)
```{r}
Estrus[["percent.mt"]] <- PercentageFeatureSet(Estrus, pattern = "^mt-")
```

```{r}
# This is the list of genes that start with mt
mito_genes <- grep("^mt-", rownames(Estrus) , ignore.case=T, value=T)
print(mito_genes)
```

```{r}
# This is the data in "percent.mt" column
percent_mito <- FetchData(object = Estrus, vars = "percent.mt") #how to fetch data
head(percent_mito)
```

```{r, fig.width=10}
# Visualize QC metrics as a violin plot
VlnPlot(Estrus, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
```
```{r}
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(Estrus, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(Estrus, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
```

## Filter out low-quality cells
* We filter out cells that have unique feature counts over 6000
* Because any cell that has more than 6000 genes are likely doublets
* Note: Seurat tutorial used 2500 instead of 6000
* We also filter out cells that have more than 5% mitochondrial genes
* Because those are likely to be nonviable cells
```{r}
previous_time <- proc.time()[3]
Estrus <- subset(Estrus, subset = nFeature_RNA < 6000 & percent.mt < 5)
proc.time()[3] - previous_time
```
```{r}
Estrus
```
* After filtering step, the dataset is left with
 + 19420 genes
 + 2174 cells

## Normalizing the data
* Perform log-normalization method
* Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor
* scale.factor default value = 10,000
* This is then natural-log transformed using log1p
```{r}
previous_time <- proc.time()[3]
Estrus <- NormalizeData(Estrus, normalization.method = "LogNormalize", scale.factor = 10000)
proc.time()[3] - previous_time
```
## Identification of highly variable features (feature selection)
```{r}
previous_time <- proc.time()[3]
Estrus <- FindVariableFeatures(Estrus, selection.method = "disp", nfeatures = 5000)
proc.time()[3] - previous_time
```
## Plotting highly variable features
```{r}
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(Estrus), 10)
top10
```

```{r, fig.width=10,fig.height=4.5}
# Plot variable features with and without labels
plot1 <- VariableFeaturePlot(Estrus)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2 
```
## Regress out data and perform scaling
* Regressing out total counts per cell and percent.mt before scaling. 
* Take out mitochondrial genes before scaling (analyzing) the data 
* Because mito_genes shouldn't be accounted for cell population analysis.
```{r}
previous_time <- proc.time()[3]
all.genes <- rownames(Estrus)
Estrus <- ScaleData(Estrus, features = all.genes, vars.to.regress = c("nCount_RNA","percent.mt"), scale.max = 10)
# Explanations below are from [Seurat](https://satijalab.org/seurat/archive/v3.0/cell_cycle_vignette.html)
# vars.to.regress = RegressOut in Seurat version 1.4 or lower
# However, as the results of this procedure are stored in the scaled data slot (therefore overwriting the output of ScaleData) 
# This functionality is merged into the ScaleData function itself.
proc.time()[3] - previous_time
```
## Perform linear dimensional reduction (PCA)
```{r}
previous_time <- proc.time()[3]
Estrus <- RunPCA(Estrus, features = VariableFeatures(object = Estrus))
proc.time()[3] - previous_time
```

## Examine and visualize PCA results a few different ways
```{r}
print(Estrus[["pca"]], dims = 1:5, nfeatures = 5)
```

```{r}
VizDimLoadings(Estrus, dims = 1:2, reduction = "pca")
```

```{r}
DimPlot(Estrus, reduction = "pca") + ggtitle(label = 'Including immune cells')
```

```{r, fig.width=12}
FeaturePlot(Estrus, reduction = "pca", features = c('Ovgp1', 
                                                    'Foxj1', 'Igf1', 'Pdxk', 
                                                    'Fxyd4', 'Bsg', 
                                                    'Serpina1e', 'Krt19'))
```
```{r, fig.width=12}
FeaturePlot(Estrus, reduction = "pca", features = c('Dcn', 'Pax8',
                                                    'Ephx2', 
                                                    'Pecam1', 'Pdgfra', 'Myh11', 'Atp2b4', 
                                                    'Cd52'))
```

## Determine the ‘dimensionality’ of the data
* Seurat tutorial uses JackStraw() and and ScoreJackStraw to determine dimensionality
* the syntax is: 
  + Estrus <- JackStraw(Estrus, num.replicate = 100)
  + Estrus <- ScoreJackStraw(Estrus, dims = 1:20)
* However, it takes very long. 
* An elbow plot often corresponds well enough with the significant dims and is much faster to run than Jackstraw
* According to the graph below, choose the first 37 principle components to use
```{r}
ElbowPlot(Estrus, ndims = 40)
```
## Find neighbors
* Dims = dimensions = determined as 37 from the above step
```{r}
previous_time <- proc.time()[3]
Estrus_new <- FindNeighbors(Estrus, dims = 1:37, k.param = 10)
proc.time()[3] - previous_time
```
## Find cell clusters using Leiden's method
```{r}
# Algorithm = 4 specifies Leiden's method
previous_time <- proc.time()[3]
Estrus_new <- FindClusters(Estrus_new, resolution = 0.12, algorithm = 4)
proc.time()[3] - previous_time
```
## Constructing UMAP plot
```{r}
previous_time <- proc.time()[3]
Estrus_new <- RunUMAP(Estrus_new, dim = 1:37) 
proc.time()[3] - previous_time
```
## Visualization
```{r}
DimPlot(Estrus_new, reduction = "umap") + ggtitle(label = 'Including immune cells')
```
```{r, fig.width=12}
FeaturePlot(Estrus_new, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Dcn', 'Serpina1e', 'Sprr2f',
                                                         'Fxyd4', 'Ly6a', 'Epcam', 'Krt19', 'Vim', 'Pdgfra'))
```

```{r, fig.width=12}
FeaturePlot(Estrus_new, reduction = "umap", features = c('Myh11', 
                                        'Dkk2', 'Slitrk6', 'Dio2', 'Smoc2', 'Tes', 'Dab1', 'Camk1d', 'Pecam1', 'Cd52','Mki67'))

```
## Removing immune cells
```{r, fig.width=12}
sub_Estrus = subset(x = Estrus_new, ident = c(1,2,3,4,5,7,8,9,10,12))
DimPlot(sub_Estrus, reduction = "umap") + ggtitle(label = 'Immune cells removed')
```
```{r, fig.width=12}
FeaturePlot(sub_Estrus, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Fxyd4', 'Cxcl17', 'Myh11', 'Cd52', 'Twist2', 'Pecam1'))
```
```{r, fig.width=12}
FeaturePlot(sub_Estrus, reduction = "umap", features = c('Pdgfra', 'Ephx2', 'Dcn', 'Krt19', 'Epcam', 'Vim','Mki67'))
```
```{r}
sub_Estrus
```
* After removing immune cells, the dataset is left with:
  + 19420 genes
  + 1925 cells

## Re-run analysis after excluding immune cells
```{r}
previous_time <- proc.time()[3]
sub_Estrus <- RunPCA(sub_Estrus, features = VariableFeatures(object = sub_Estrus))
proc.time()[3] - previous_time
```

```{r, fig.width=12}
DimPlot(sub_Estrus, reduction = "pca") + ggtitle(label = 'Immune cells removed')
```

```{r, fig.width=12}
FeaturePlot(sub_Estrus, reduction = "pca", features = c('Esr1', 'Ovgp1', 'Foxj1', 'Fxyd4', 'Ephx2', 'Dcn'))
```

```{r, fig.width=12}
FeaturePlot(sub_Estrus, reduction = "pca", features = c('Fos', 'Kcne3', 'Serpina1e', 'Crabp2', 'Pgr', 'Vim'))
```
```{r}
previous_time <- proc.time()[3]
sub_Estrus_new <- FindNeighbors(sub_Estrus, dims = 1:37)
proc.time()[3] - previous_time
```

```{r}
previous_time <- proc.time()[3]
sub_Estrus_new <- FindClusters(sub_Estrus_new, resolution = 0.045, algorithm = 4)
proc.time()[3] - previous_time
```

```{r}
previous_time <- proc.time()[3]
sub_Estrus_new <- RunUMAP(sub_Estrus_new, dim = 1:37)
proc.time()[3] - previous_time
```

```{r, fig.width=10}
DimPlot(sub_Estrus_new, reduction = "umap") + ggtitle(label = 'Rescale UMAP after removing Immune cells')
```
```{r, fig.width=12}
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Ovgp1', 'Foxj1', 'Crabp2', 'Myh11', 'Wt1', 'S100g', 'Sprr2f',
                  'Fxyd4'))
```

```{r, fig.width=12}
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Dcn', 'Pax8', 'Pax2', 'Twist2', 'Igf1', 'Serpina1e', 'Pdgfra', 'Krt19', 'Mki67'))
```
## Marker genes
```{r}
marker_genes = c('Epcam', 'Krt15','Krt8','Krt19','Vim','Pdgfra','Twist2', 'Sprr2f', 'Fxyd4', 'Igfbp6',
                 'Acta2', 'Myh11','Foxj1','Ovgp1','Wt1','Serpina1e','S100g','Pecam1')
```

## UMAP for marker genes
```{r, fig.width=12}
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Epcam', 'Krt15','Krt8','Krt19','Vim','Pdgfra','Twist2', 'Sprr2f', 'Fxyd4'))
```

```{r, fig.width=12}
FeaturePlot(sub_Estrus_new, reduction = "umap", features = c('Igfbp6',
                                                             'Acta2', 'Myh11','Foxj1','Ovgp1','Wt1','Serpina1e','S100g','Pecam1'))
```

## Rename clusters
```{r}
Cluster.names <- c('1 Secretory_InfAmp',
                   '2 Ciliated_InfAmp',
                   '3 Fibroblast Pdgfra+',
                   '4 Secretory_IsthUTJ',
                   '5 Fibroblast Pdgfra-',
                   '6 Muscle')
names(Cluster.names) <- levels(sub_Estrus_new)
sub_Estrus_new <- RenameIdents(sub_Estrus_new, Cluster.names)
DimPlot(sub_Estrus_new, reduction = "umap", pt.size = 0.5) + scale_color_manual(labels = Cluster.names,values=c('green',                                                                                        'red','blue','orange','grey','yellow','pink'))
```
```{r, fig.width=12}
DotPlot(sub_Estrus_new, features = marker_genes, group.by = sub_Estrus_new@meta.data$ident) +  
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  scale_y_discrete(labels = Cluster.names)
```
## Number of cells in each clusters
```{r}
table(sub_Estrus_new@active.ident, sub_Estrus_new@meta.data$orig.ident)
```

## Extract top genes for each cluster
* Default method of finding highly expressed genes for FindAllMarkers is Wilcoxon Rank Sum test
```{r}
sub_Estrus_new_markers <- FindAllMarkers(sub_Estrus_new, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
sub_Estrus_new_markers %>%
    group_by(cluster) %>%
    slice_max(n = 20, order_by = avg_log2FC)
```
## Heat map for top 10 genes in each cluster
```{r, fig.width=10}
sub_Estrus_new_markers %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log2FC) -> top10
DoHeatmap(sub_Estrus_new, features = top10$gene) + NoLegend()
```

